Abstract

Two profiles (P1 and P2) were established along two lines perpendicular to the direction of the Luan River flow and 200 pore water and sediments samples as well as thirty maize samples, were collected from ten drills in these two profiles. Spatial distribution models of iron, manganese and lead were established using the Simple Kriging method, and the transfer factor (TF) and multi-metal (combined) target hazard quotient (CTHQ) were calculated to assess the potential human health risk of the metals. The results indicate that the maximum TF value was 3245.37 for iron in the sheath, while its min value was 13 for lead in the seeds. Of the ten drills, Sd08 possessed the maximum CTHQ (1.74), while the CTHQ of the other drills was less than the unit, indicating that it is not likely to impact human health. This work indicates that the Luan River and the associated irrigation water have significant effects on the spatial distribution of the three metals in the critical zone (CZ). Iron exhibited greater transfer ability than manganese and lead, however it was mainly concentrated in the roots and sheath of the maize, rather than the seeds. The human health risks of metals are attributed to lead.

Due to rapid economic and industrial development during the last few decades, heavy metal water contamination has become a serious global problem. The cities of Tangshan and Qianan, located in the Hebei province in the North China Plain (NCP) have become well-known for their iron mineral resources since the 1950s. The Luan River is situated on the eastern boundary of these two cities, and as a result of pollution from industrial and domestic wastewater, the quality of the Luan River has deteriorated. This is detrimental for the local ecological environment. In this region, the irrigation water is derived from three sources, namely the Luan River, shallow groundwater and precipitation. The quality of the shallow groundwater is significantly affected by the Luan River due to the lateral recharge, especially in the river bank area. Under the effects of contaminated surface and groundwater as a result of irrigation practices, the solutes and energy that is migrated from the irrigation water to vegetables, soil, vadose zone and aquifer layers strongly affects the ecological environment (Koc 2015). Importantly, heavy metals can accumulate in the edible parts of crops, posing considerable health risks to humans and animals. A large number of researchers have investigated the effects of contaminated irrigation water and the river on the spatial variation of heavy metals in aquifers, vadose zones, soils, and vegetable layers. For example, Rahman et al. (2016) established a new model to simulate the hydrological interactions between rivers and groundwater. Qureshi et al. (2016) investigated the concentrations of iron, copper, chromium and zinc in vegetables and soil in order to assess the potential health risk of eating vegetables irrigated with treated wastewater. Ahmad & Goni (2010) analyzed fifty soil samples collected from the Dhaka Export Processing Zone, and their results indicated that the soil layer was contaminated with iron and cadmium through the repeated use of wastewater from industries and other sources. Yabusaki et al. (2008) built conceptual models of uranium sorption to elevate the representation of uranium transport processes in the river-aquifer-vadose layers. Contaminated surface water and industrial wastewater are often associated with vegetable, soil, vadose and aquifer layers, and there are close connections among these layers. Nevertheless, most of these studies only considered the interaction between surface water and a single layer separately; information regarding the interactions among these layers is lacking. All these layers should be considered as an integral system. Therefore, a critical zone (CZ), defined by the National Research Council's Committee (NRC) on Basic Research Opportunities in the Earth Sciences, was introduced into this work and encompasses the vegetation canopy through to the lower depths of aquifers (Goldhaber et al. 2014).

Excessive accumulation of heavy metals in surface and groundwater may not only cause soil, vadose zone and aquifer contamination, but may also affect vegetable quality and safety. There are large farmlands in the Luan River catchment that produce most of the crop products for millions of residents in nearby cities. Previous studies have revealed that the dietary intake of contaminated crops and vegetables is the main route, apart from occupational exposure, for human intake of heavy metals (Garg et al. 2014), which can damage to the nervous, skeletal, circulatory, enzymatic, endocrine and immune systems. With the exception of irrigation water, heavy metal contents in vegetables are affected by others factors, such as climatic conditions, soil structure, soil water, irrigation model and fertilizer consumption (Löf et al. 2011; Vogtmann et al. 2013; Verma et al. 2015; Noli & Tsamos 2016; Shaheen et al. 2016). Identifying the main influencing factors of metals is necessary for preventing contamination, which can efficiently reduce the heavy metal content in vegetables.

Therefore, the objectives of this study were to characterize the spatial distribution of heavy metals in the CZ under the influences of vertical infiltration of irrigation water and lateral recharge of the Luan River, identify the most important influencing factors of heavy metals in vegetables, and assess the potential human health risks of heavy metal uptake through vegetable consumption. The outcome of this work is expected to provide necessary information to inform policy makers for ecological environment management in NCP.

Materials and Methods

Study Area

The Luan River basin is located in the NCP between a latitude of 39°44′–42°44′ N and longitude of 115°33′–119°36′ E. This river originates in the Mongolia plateau, with a total area of 44 750 km2, accounting for 14.06% of the entire area of the Hai River basin (Wang et al. 2016), joining the Bohai Sea at Leting County after 888 km. The Luan River plays an important role in Tangshan, Qinghuangdao, Chende and Tianjin in terms of economic development (Zhang et al. 2016). The study area of this work is located in the lower part of the Luan River and lies between 39°30′–39°40′ N in latitude and 118°45′–119°00′ E in longitude in the southeastern Hebei province (Fig. 1). The total area of this region is c. 420 km2. The topography is characterized by low hills with elevations ranging from 24 to 56 m above sea level. The region slopes downward from the northern piedmont to the southwestern alluvial-pluvial plain. The temperature of this region varies between −11 and 30°C with a mean annual air temperature of 10.1°C. The geological environment of this region is described as alluvial-fluvial sedimentary and consists of silt soil, silt clay, silt sand, fine sand and medium sand. According to the lithology and sedimentary sequences of the study area, this region can be divided into unconfined and confined aquifers. The unconfined aquifers include three layers, and the burial depth of the first layer is 6 – 10 m. Luan River, irrigation water and precipitation are the main recharge sources for this shallow aquifer, and the water table decreases from 2.7 m near the Luan River to 7.6 m in southwestern and southeastern parts of the study area.

Samples

In order to characterize the spatial distribution of heavy metals in the CZ of the Luan River catchment, two profiles (P1 and P2) were established along two lines perpendicular to the direction of the stream flow. Each profile included five boreholes, and the depths of these drills were 30 m. In these two profiles, the interval of each drill was about 300 m and both the distance between the first borehole of this profile (Sd08 or Sd01) and the Luan River was 350 m. A total of 200 sediment samples were collected from ten drills and the samples were collected from the top 20 cm of each surface layer at an interval of 1.5 m until 30 m and comprised an inner diameter of 11 cm and a length of 60 cm. Each sample was sealed in a clean polyethylene drum and placed in a cool box on site, after which they were transported to the laboratory to obtain pore water using a pressure of 1000 – 1500 kPa. Typically, 60 – 100 ml water could be stressed from each sediment sample. Nine hydrochemistry variables (TDS, Fe, Cu, Pb, Zn, Mn, Ni, As and pH) were analyzed within 24 h for this pore water, while seven chemical elements (Fe, Cu, Pb, Zn, Mn, Ni and As) and grain size were analyzed for the sediment samples. In addition, at a depth of 0 – 2 m, twenty samples were collected from each drill and analyzed for physical parameters, such as soil organic carbon (SOC), total nitrogen (TN), temperature (T), pH, salt and water content (WC).

Maize is widely cultivated throughout the world, and a greater weight of maize is produced than any other grain. China produces 21.42% of the global harvest, and one third of China's total maize are located in the NCP. Maize is the third ranked cereal in China following wheat and rice and is widely used as animal fodder and as a principal raw material for industrial products. Three tissue samples were collected from the middle leaf sheath, brace roots and the top ears of each maize plant, which were located at the corresponding drill sites. All thirty maize samples were kept in clean polyethylene bags and brought to the laboratory to determine heavy metal content.

Additional, six water samples were also collected from the Luan River and shallow groundwater in July, September and November in 2015, respectively. The shallow groundwater samples were collected from well W01. The depth of this well was 10 m. All the water samples were subjected to chemical analysis at the Institute of Hydrogeology and Environment Geology. Information regarding heavy metals in the CZ can be better understood by studying these vegetable, soil and water samples.

The CZ concept, as defined by Lybrand & Rasmussen (2014), ranged from the vegetable canyon to the bottom of the bedrock. In this work, maize was used as the research object for surface vegetation, typically measuring 2 m in height. The burial depth of the bottom of the first shallow aquifer is about 10 m, which is the main explored layer and significantly affected by the Luan River. Therefore, the length of the CZ is defined as 12 m in this work, which is the sum of the maize height and the burial depth of the aquifer, and includes the maize, soil, vadose zone and shallow aquifer layers in a vertical direction.

Methods

The objective of this article was to characterize the heavy metals spatial variation in the CZ and assess the human health risks associated with the consumption of contaminated maize. The next section describes the details of this procedure and involves the following steps. The first step was to characterize the space and temporal variation in the hydrochemistry variables of the Luan River and the shallow groundwater in order to understand the basic information of water quality of this region. Subsequently, the vertical variation of heavy metals in the CZ for each drill was described, and then their spatial distribution in the CZ of the P1 and P2 profiles were established using the Simple Kriging (SK) method. It is known that a large number of factors such as pH, cation exchange capacity (CEC), clay minerals, organic matter and water content can affect the migration of trace elements in vegetables (Qureshi et al. 2016). The second step was to identify the most important influential factors from the aforementioned soil chemistry and physical properties using the random forest (RF) method, after which path analysis was used to determine how the main influencing factor transmits its effects to heavy metal contents in maize (Gargoum & El-Basyouny 2016). Path analysis, developed by Wright (1960), is a straightforward extension of multiple regression that can provide estimates of the magnitude and significance of hypothesized causal connections between sets of influencing factors through the path diagram. The third step was to calculate the transfer factor (TF) and the target hazard quotients (THQ) of the consumed maize from these two profiles, providing information for food safety management.

The TF of heavy metals from soils to maize of this region were calculated using the following equations (Verma et al. 2015):(1)Where Cmaize refers to metal concentration in maize organs (per dry weight) and Csoil is the concentration of the element in the soil (dry weight) where the plant is grown. To assess the health risks associated with heavy metal contamination, target hazard quotients (THQ) were calculated using the following equations as reported by Noli & Tsamos (2016):(2)Where Ci (in mg/kg) is the concentration of the ith (i = 1, 2,…, n; n is the number of metals) metal in the seeds, Con (in kg/person/day) is the daily average consumption of seeds in this area, Bw (in kg/person) represents body weight, EF is exposure frequency (365 d/yr), ED is exposure duration (70 yr, equivalent to the average lifespan) and AT is average time (365 d/yr number of exposure years, assuming 70 yr in this study). The average daily maize intakes of adults in the NCP are believed to be 0.15 kg/person/d (Yang et al. 2011). Bodyweight, referred to as the average bodyweight of a male in the NCP (70 kg) and the tolerable upper intake level (UL) of the ith metal was taken as Rfdi, except for lead, for which the provisional tolerable daily intake value was considered (Qureshi et al. 2016). Previous studies have indicated that exposure to two or more pollutants may result in additive and/or interactive effects (Wang et al. 2005). To evaluate the health risks to human of multiple metals together in seeds, the multi-metal (combined) target hazard quotient (CTHQ) was calculated by using formula 3 (Cherfi et al. 2015), which is the mathematical sum of individual THQi of all studied metals:(3)

Results

In this work, the surface, ground and pore water, soil and vegetable samples were subjected to chemistry analysis. In comparison with the standard guidelines for drinking water and vegetable safety, it was found that the concentrations of manganese, iron and lead were higher than their recommended permissible values (World Health Organization (WHO) 2004). These three metals were selected and their spatial distribution model in the CZ established in this article, assessing the potential risk to human health. Figure 2 depicts their contents in the Luan River and shallow groundwater from July to November in 2015. The concentrations of iron in the Luan River and groundwater far exceeded its standard guidelines for drinking water (0.3 mg/l). The maximum iron value was 1.81 mg/l in groundwater and 3.09 mg/l in Luan River in July, which was 6.03 times greater than standard value for groundwater and 10.3 times for Luan River. The manganese content in the Luan River was less than the guideline value (0.1 mg/l), however its concentration in the groundwater was higher than the standard value, with a maximum value of 3.05 mg/l in September 2015.

The concentrations of manganese, iron and lead in the Luan River and shallow groundwater.

Using the above analysis, the basic hydrochemistry information of shallow aquifers and the Luan River in the CZ was determined. Both the concentrations of lead in the Luan River and groundwater did not exceed the standard value (0.05 mg/l), however its concentrations in different maize organs were far higher than the guideline values (Fig. 3). It was found that maize contamination with respect to manganese, iron and lead in P1 was more serious than that in P2. The maximum values of manganese, iron and lead were 255.21, 2904.61 and 3.80 mg/kg in the P1 profile, which were all located at the Sd10 drill and were far higher than at the others boreholes. In the P2 profile, the maximum values of these three metals were 81.60, 160.80 and 2.79 mg/kg, which were all distributed at the Sd04 drill. It should be noted that the difference in the above three ions in the P2 profiles were small and exhibited weaker variation than P1. In order to understand the migration of metals in the CZ, the concentration of metal in the pore water, sediment and maize are presented in Figure 4. For the length of this paper, this figure only demonstrates the vertical variation in lead in the CZ for the ten drills.

Vertical variation of lead in maize, sediment and pore water in the CZ for ten drills. Dashed lines represent the concentration of lead in maize and sediments; colour maps show the lead content in pore water.

In Figure 4, the dashed lines represent the concentration of lead in maize and sediments and the colour maps demonstrate the lead content in the pore water. This figure reports that the maximum values of lead in vegetables, sediments and pore water were 3.801, 27.26 and 0.034 mg/l, respectively. With regards to pore water, the maximum value of lead was distributed at different depths among the ten drills; specifically, at a depth of 11 – 12 m for Sd05, Sd08 and Sd09, 6 – 8.5 m for Sd01, Sd03, Sd11 and Sd12 and 2 – 3 m for Sd02, Sd04 and Sd10. With respect to the lead content in sediments and maize, the changing trend could be divided into two models: the first was a decreasing model, i.e. the lead concentration decreased with depth, and the second was similar to the Gaussian model, in that the lead content increased from 2 to 6 m, and then decreased from 6 to 12 m, and therefore its maximum value was mainly distributed at the depth of 3 – 7 m.

Basic information regarding manganese, iron and lead contents in the CZ of study area was obtained, and then their spatial distribution models were established using the SK method (Fig. 5). This figure indicates that manganese, iron and lead in vegetables (V-prefix) and sediments (S-prefix) exhibited a similar distribution, with the elements migrating from Sd10 to two terminals of the P1 profile. However, these ions in the pore water (W-prefix) were transported from Sd12 and Sd08 to the middle section of the P1 profile, which was contrary to what was found in the sediments and maize. All the maximum values of manganese, iron and lead were distributed at Sd02 and Sd04 in the P2 profile, and exhibited a similar variation between sediments and maize. In both the P1 and P2 profiles, these three elements had opposite distributions between maize and pore water, indicating that they had a stronger negative correlation. Vegetables play an important role for human beings. The information regarding the three variables accumulated in maize was understood using the above analysis. It is necessary to identify their main influencing factors in order to inform maize metal pollution prevention. The concentrations of metals in maize can be affected by multiple factors including irrigation model, fertilizer use, climate conditions, CZ structure, chemical and physical parameters. The distances of P1 and P2 were only 10 km in this work and there were no significant difference in the fertilizer use, irrigation model and climate conditions, and therefore the Gini index in the RF method was used to identify the most important influential factors from the CZ structure parameters (mean grain size, non-uniform coefficient (Cu), clay, silt and sand fraction content), physical parameters (salt, SOC, TN, T, WC and pH) and chemical parameters (the manganese, iron and lead contents in pore water (W-Mn, W-Fe and W-Pb) and sediments (S-Mn, S-Fe and S-Pb)). The Gini index was employed to quantitatively describe the importance of thirteen influencing factors for manganese, iron and lead in maize for the single drill (Table 1 and Fig. S1). When the depth of the CZ parameters was larger than 2 m, which were difficult to affect the growth characteristic of maize (Liu et al. 2009), the CZ physical parameters were only collected within the 0 – 2 m layer. The most important influencing factors for the three metals were collected from the CZ parameters within the 0 – 2 m layer. This result reflects that the most important influencing factors of the three metals in maize included clay fraction content, mean grain size and the hydrochemistry of pore water. Of the ten drills, the percentages of clay, mean grain size and chemistry parameters were 30, 20 and 20% for manganese in maize (V-Mn), 30, 30 and 20% for iron in maize (V-Fe) and 20, 30 and 30% for lead in maize (V-Pb).

The importance of thirteen parameters for manganese, iron and lead contents in maize for ten drills

Table 2 describes the important influencing factors of the three metals in maize for the P1 and P2 profiles (Fig. S2). In the P1 profile, the main influencing factor of the three metals in maize were W-Mn and T, respectively, while in the P2 profile, sand fraction content, W-Fe and mean grain size were the main influencing factors. There were significant differences in the main influencing factors between the profiles and the single drill, and the importance of the clay fraction content in these two profiles became weaker in comparison with the single borehole. This difference indicates that fine particles play an important role in metals concentration in maize with respect to the single drill, which become coarse particles for profiles. Figure 6 represents the manganese, iron and lead contents, and the spatial distribution of their main influencing factors. With respect to the P1 profile, the T of the soil layer plays an important role in lead and iron concentrations in maize, which decreased from Sd10 to Sd12 and Sd08. With regards to the P2 profile, the spatial distribution of mean grain size, W-Fe and sand fraction content were similar, and their maximum values were mainly distributed at Sd03. Although the importance of these influencing factors has been quantitatively described using the RF method, the manner in which these factors transmit their effects to the selected metal contents in maize are unknown. For example, the T of the soil layer had an important impact on iron and lead contents in maize, and this effect could be divided into two groups, i. e. direct and indirect effects. The former suggests that T can directly affect the cell activity of the maize roots, while the latter suggests that T can affect through the others factors, such as SOC, WC, and salt, to V-Fe and V-Pb. In Figure 7, the main influencing factor is the input variable, and the output variables were V-Mn, V-Fe and V-Pb, and then the other variables, such as SOC, TN, pH and salt were used to connect the input and output variables using the path analysis method. In the P1 profile, the direct path coefficient of T on V-Fe was 0.703. While T can accelerate the uptake of iron in maize, on the contrary, it can also through the other parameters reduce the bioaccumulation of iron in maize as the indirect path coefficient was −0.046. Therefore, the total effect of T on iron was 0.657. The direct and total effects of W-Mn on V-Mn were −0.512 and −0.383, respectively. There was a negative correlation between W-Mn and V-Mn, which was consistent with the above analysis. In the P2 profile, the direct path coefficient of mean grain size on V-Pb was only 0.028. On the contrary, its indirect path coefficients through silt and sand fraction contents were 17.831 and −19.368. These values were much higher than the other factors, indicating that mean grain size, mainly through silt and sand fraction content, transmitted effects to V-Pb. More importantly, both the indirect path coefficients of mean grain size through clay and silt fraction content to V-Pb were positive, but were negative through the sand fraction content, reflecting coarse particles can reduce the uptake of lead in maize.

The importance of thirteen parameters for manganese, iron and lead contents in maize for P1 and P2 profiles

The main influencing factors of metals in maize were identified and their effect paths were described, providing sufficient information to prevent vegetable contamination. Therefore, the final step was to assess the health risks of manganese, iron and lead to humans. The traditional methods employ the concentration of metal in soil to calculate the TF value, however, the above results indicate that the effects of manganese, iron and lead were much larger in pore water than in soil. Therefore the concentration of metal in pore water instead of the soil was used to calculate the TF value for maize using Equation 1. The unit of metal in pore water was mg/l, the density of water was considered to be 1000 mg/l, the metal concentration in pore water can convert to mg/kg and Table 3 shows the TF values of the three metals for the ten drills.

In this table, the maximum value of TF for manganese was 3190.18 in the sheath, 3245.37 for iron in the root and 292.38 for lead in the sheath. All of these maximum values appeared at Sd10. The TF value of manganese in the P1 profile was higher than in P2, while the TF value of iron in P1 was smaller than P2, and there were no significant differences in the TF of lead between P1 and P2. This figure reflects that manganese, iron and lead in pore water possessed a stronger transfer ability from water to the root cell of maize, which can cause various toxic effects to human health. Figure 8 describes the CTHQ of the maize seeds for the ten drills. The maximum value was 1.74 in Sd08 and the min value was 0.34 in Sd10. With the exception of Sd08, all the CTHQ values of the others drill were less than 1. The order of CTHQ in P1 was Sd08 > Sd11 > Sd09 > Sd12 > Sd10, while in the P2 profile it was Sd03 > Sd02 > Sd01 > Sd04 > Sd05.

Multiple health risk index of manganese, iron and lead for the ten drills.

Discussion

The Luan River significantly affects shallow groundwater quality as a result of lateral recharge. Figure 2 demonstrates that the iron content in the Luan River decreased from 3.09 mg/l in July to 0.29 mg/l in November and a decreasing trend with time was also observed in the groundwater. The iron content in both the Luan River and groundwater in July and September were far higher than that in November. This can mainly be attributed to the fact that summer is the primary production season for the iron industry and therefore a large amount of industrial wastewater is discharged into the Luan River. As production decreases in winter, the iron content is therefore lower in both the Luan River and groundwater in November. The iron element in the groundwater was mainly influenced by the Luan River and therefore it demonstrated a similar tendency as the Luan River. The manganese concentration in the Luan River was less than the standard value and also presented a decreasing trend with time as observed with iron. On the contrary, it was at a higher level in the shallow groundwater, indicating there are other manganese material sources in the groundwater apart from the Luan River. The lead content in the Luan River was higher than that in the groundwater; however, neither of them exceeded the standard value. However, the lead concentration in the different maize organs was higher than the guideline values, with the exception of the seeds samples at Sd10. The iron content of the edible part of maize in P1 was less than 20 mg/kg, while in the P2 profile it was far higher than the standard value. All three metals contents in the sheath exceeded their guideline values. As this constitutes the main feedstuff of livestock in NCP, this might result in metal bioaccumulation in livestock, posing indirect health risks to humans.

Figure 4 describes the vertical variation in lead content in the CZ. It was found that the W-Pb content increased with depth at Sd08 and Sd05 with a maximum value recorded at a depth of 10 – 12 m, which was associated with the Luan River and groundwater. The concentration of W-Pb decreased with depth at Sd02 with a maximum value recorded at a depth of 2 – 4 m, suggesting that this lead could be attributed to irrigation water as it migrated from the soil and vadose zone to aquifer. At Sd01, Sd03, Sd11 and Sd12, the maximum value of W-Pb was distributed at a depth of 4 – 9 m, while at Sd04, this depth had the min W-Pb value recorded. In general, the vertical variation in V-Pb in maize is simple and conforms to an elongated model, with the exception of Sd05 and Sd08 where the V-Pb content decreased with depth. S-Pb increased with depth in Sd02 and decreased with depth in Sd11, while its vertical variation in the other drills was complex. No close associations in vertical variation existed between W-Pb and S-Pb, indicating that the lead element possessed different transfer models in sediments and pore water. However, a strong negative relationship existed between W-Pb and S-Pb in Sd02, in which the lead ion migrated from the top surface to the aquifer layer in the pore water, resulting in its concentration decreasing with depth due to the adsorption of sediments, and therefore the lead ion in the sediments increasing with depth.

Figure 5 shows that the distribution models of manganese, iron and lead in pore water are similar, as their lower values are mainly located in the middle section of the P1 profile. These elements were derived from the Luan River. The migration ranges of manganese and lead were larger than that of iron. The concentrations of S-Mn and S-Fe were higher at the depth of 10 – 12 m in Sd11 where the lower value of S-Pb was recorded. Figure 4 indicates that there is no close association in the vertical variation of the three metals between the pore water and sediments in the single drill, with the exception of Sd02. However, Figure 5 demonstrates that the pore water and sediments exhibited a stronger positive horizontal correlation of iron and manganese, which was negatively correlated with lead. These three metals in the pore water exhibited a similar distribution, especially between manganese and iron in the P2 profile where their maximum value was mainly distributed at Sd03 and Sd05. Although the distance of these two profiles was only 10 km, the concentrations of V-Mn, V-Fe and V-Pb in P2 were far less than in P1. The content of V-Pb was relatively lower in P2, however its pollution range was distributed more widely as 80% of region possessed a higher V-Pb value. Metal accumulation in vegetables is affected by numerous factors, with the exception of pore water and sediment chemical properties. Table 2 characterizes the importance of each influencing factor for manganese, iron and lead in maize. Compared with the CZ chemical and physical properties, the CZ structure parameters, particularly the fine particles, were more important for the content of the three metals in maize. It was discovered that the importance of WC, pH, T, SOC and salt of the CZ for these three metals in maize was very small. However, these parameters have a close relationship with biological activities in the rhizosphere of maize, which can affect the migration of metals (Martínez-Alcalá et al. 2010; Huang et al. 2017). When the study region extended to the single drill, their importance decreased and was replaced by CZ structure parameters. More importantly, when the study region encompassed the P1 and P2 profiles, the main influencing factors of V-Mn, V-Fe and V-Pb changed. T became the most important influencing factor for iron and lead in maize once again (Table 2), while salt, pH and WC still had nothing significant effects on these three metals (Fig. S2). This result reflects the differences in the main influencing factors of elements in maize with respect to study area scale. Figure 6 indicates the spatial distribution of metal in maize and their main controlling factors. It can be observed from this figure that the metal content in maize was higher when the influencing factors were small, with the exception of T, indicating that they had a negative correlation. Although the metal in the soil and pore water was the main material source of metal in the vegetables, the maize metal pollution is not directly linked to the contamination of sediments and pore water (Cao et al. 2016). The metal concentration in vegetables is affected by others factors and therefore, even if the metal content in sediments and pore water is lower, they can have higher concentrations in maize due to their non-biodegradable and persistent nature.

Figure 7 depicts the influence path of the main factors on the selected metals in maize. The direct and indirect effects can be cut off to prevent the uptake and bioaccumulation of metals in maize. This figure reports that all the total effects coefficients of W-Mn, W-Fe, mean grain size and sand fraction content were negative while, on the contrary, both the total effects coefficients of T for V-Fe and V-Pb were positive, which was consistent with the above analysis. The direct path coefficient of T on V-Fe was 0.703. Of the twelve indirect influencing factors, the maximum absolute value of the indirect path coefficient was −0.364 for silt fraction content. The direct path coefficient of T on V-Pb was 0.387, and the indirect path coefficient of T through silt fraction content to V-Pb was 0.272. This result indicates that the strata with higher T can increase the bioaccumulation of lead and iron in maize and the region with higher silt fraction content can decrease V-Fe, while the area with lower silt fraction content can decrease V-Pb. The silt fraction content is an efficient tool to decrease lead and iron contents in maize due to its higher indirect path coefficients. In the P2 profile, the sand fraction content was the main influencing factor of V-Mn and its indirect path coefficient was higher for W-Fe and mean grain size. All of these values were negative, indicating that the region with the higher sand fraction content could decrease the uptake of manganese, iron and lead in maize. Traditional methods use micro factors to decrease metal contents in vegetables. This work can provide a macro strategy from a hydrogeological perspective to prevent metal biomass in vegetables in large areas according to the CZ systems. For example, in the P2 profile, the local farmer could plant the maize in the point bar microfacies with a higher sand fraction content, the coarse particles can prevent uptake of manganese, iron and lead. Conversely, in the P1 profile, maize can be planted in the flood plain and fan-between depression where there is a lower silt fraction content, thereby decreasing bioaccumulation of lead.

As observed in Table 3, the general trends in the TF of the three metals in the two profiles decreased in the order of iron > manganese > lead. Furthermore, the TF values of iron varied from the highest (3245.37) in the roots to the lowest (16.88) in the seeds, with an average value of 351.53. The TF value of manganese ranged from 21.12 in the seeds to 3190.18 in the sheath, with a mean value of 261.89. The TF value of lead ranged between 13 in the seed and 292.38 in the sheath, with an average value of 134.40. This result reflects that iron and manganese are easily transferred to the plant due to greater capacity to form strong bonds with enzymes, while the transfer of lead from the pore water into the maize faced greater resistance (Qureshi et al. 2016). In addition, iron and manganese are essential elements for vegetables and are more actively absorbed from pore water by the root system (Sakizadeh et al. 2016). The trends in the TF for the three metals in the different parts of the maize decreased in the order of sheath > root > ear. As found in the present study, higher TF values in the sheath were also reported by Su et al. (2011). It is generally believed that the sheath has a higher TF value because metals combine more easily with the cellulose and lignin of cell walls, which are mainly distributed at the sheath due to the fibrous root system of maize (Chen et al. 2006). A lower TF value for the three metals in the seeds is in agreement with the results of Lu et al. (2015). The reason for this result is that when the transfer direction is from the root to the ear, metal is prone to accumulate in the roots and sheath, and therefore metal concentration in the ear is lower than in the other organs.

Although iron and manganese are essential metals for human health, excess consumption of these elements is a potential health risk. Lead is not an essential element for humans and once it enters to the food chain and accumulates at a high level in humans, it can cause severe damage to the brain, kidneys and nervous systems, as well as gastrointestinal cancer due to lack of proper mechanisms for lead removal from the human body. As shown in Figure 8, the maize seeds exhibited higher CTHQ (1.74) in Sd08 and lower CTHQ (0.34) in Sd10. Although the metal contents of maize in Sd10 were higher, these elements were mainly concentrated in the sheath and roots. Their concentrations in the seeds were lower, with a CTHQ of only 0.34. CTHQ was a synthesis index of THQ for manganese, iron and lead in this paper. The differences in the CTHQ of these drills were largely attributed to the different contributions of lead. The contribution of lead to the CTHQ ranged from 26.69 to 88.37%, with an average value of 53.95%, while the contribution of iron ranged between 5.95 and 44.87% with a mean value of 25.34%. The present results indicate that lead was the major variable potentially contributing to health risks, with iron ranking second in importance. Lead pollution in maize is thus the primary problem facing the local inhabitants. Among the ten drills, with the exception of Sd08, all the CTHQ and THQ values for iron, manganese and lead were less than 1, indicating that the local citizens consuming these vegetables will not be exposed to a potential health risk. However, some attention should be paid to Sd11 and Sd03 as their CTHQ values were not far below the threshold unit value.

Conclusion

This article established a spatial distribution model for manganese, iron and lead in the CZ. The main influencing factors of these three metals in maize were identified using the RF method and the potential human health risks of manganese, iron and lead were assessed. Based on this investigation, the following specific conclusions can be drawn:

The iron and lead elements in groundwater are mainly derived from the Luan River. Manganese in the groundwater was far higher than that in the Luan River, and therefore has alternative material sources.

The main influencing factors of these metals in vegetables are different across the study region due to scale effects. In general, the CZ structure was more important than its chemistry and physical parameters in this Luan River catchment.

An integrated alluvial-pluvial fan is developed from the piedmont to the coastal region in the Luan river catchment. Although both the P1 and P2 profiles located in the middle fan, the P2 profile distributes more closely to the fan root, and therefore their grain size is relative larger. These two profiles can represent the typical sedimentary environment of the larger river in NCP, which are formed by the same sedimentary subfacies with a small difference in grain size distribution. The results of these two profiles can be applied to other regions. According to the CZ systems, a macro strategy can be used to prevent vegetable contamination based on the sedimentary environment. For example, in the P2 profile, maize can be planted in the point bar microfacies with higher sand fraction content, which can decrease the manganese, iron and lead contents. This work has proved there is significant relation between sedimentary environment and heavy metals in the large area, the food security problem can be solved through the sedimentary environment studies, which is convenient and useful for policy maker.

The maize planted at Sd08 is not suitable for consumption due to the metal contamination; the plants located in Sd11 and Sd03 may pose health risk to human body as the CTHQ is close to unit threshold.

Metal contamination in maize seeds is mainly attributed to lead, as manganese and iron are mainly distributed in the sheath and roots of maize. This constitutes the main feedstuff of the livestock in NCP, and therefore metals can enter indirectly into food chain and pose threats to public health via meet consumption.

Funding

This work was supported by the National Natural Science Foundation of China (41502248 and 41672241) and Geological Survey Projects Foundation of Institute of Hydrogeology and Environmental Geology (G201605 and SK201504).

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